Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors.

Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals.

Considering similarity and the rating conversion of neighbors on neural collaborative filtering.

One of the most popular recommender system techniques is collaborative filtering (CF). Nowadays, many researchers apply a neural network with CF, but few focus on the neighbors' concept of CF. This work needs to consider two major issues: the similarity levels between the neighbors and the target user and the user's rating pattern conversion.

A clique-based method using dynamic programming for computing edit distance between unordered trees.

Many kinds of tree-structured data, such as RNA secondary structures, have become available due to the progress of techniques in the field of molecular biology. To analyze the tree-structured data, various measures for computing the similarity between them have been developed and applied. Among them, tree edit distance is one of the most widely used measures.

A clique-based method for the edit distance between unordered trees and its application to analysis of glycan structures.

Background: Measuring similarities between tree structured data is important for analysis of RNA secondary structures, phylogenetic trees, glycan structures, and vascular trees. The edit distance is one of the most widely used measures for comparison of tree structured data. However, it is known that computation of the edit distance for rooted unordered trees is NP-hard.